Tracking the development of co-management: using network analysis in a case from the Canadian Arctic
Bibliographic record
Abstract
ABSTRACT To understand the interplay of factors that shape changes in management strategies, we tracked the evolution of beluga whale co-management involving the Department of Fisheries and Oceans Canada, the Fisheries Joint Management Committee (FJMC), and the Tuktoyaktuk Hunter and Trapper Committee from its beginnings in the mid-1980s to the present. The objective was to analyse changes over time in the communication network involved in dealing with the Husky Lakes beluga entrapment issue, using social network analysis (SNA). Along with qualitative information, the use of SNA provided quantitative data to document the development of co-management over time. According to both government and indigenous parties, a fully functional problem-solving partnership developed over the course of two decades. Using the beluga case as the illustration, we traced the development of joint management processes, overcoming some of the initial obstacles and accommodating the needs of the various parties. This case demonstrates the importance of legal arrangements (the indigenous land claims agreement), the role of key individuals and the bridging organisation (FJMC) created by the agreement, and the maturation of co-management over time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".